Mid Level Chicago Startup and Tech Jobs

4

Informatics Scientist - Cardiology AI

Tempus
Chicago
6 days ago
+34
We are seeking a highly motivated and capable informatics scientist with extensive experience and interest in healthcare statistical analysis to join our team and lead our efforts to secure regulatory approval for novel AI algorithms in cardiology. This position requires experience with algorithm validation, statistical analysis, scientific writing and algorithm validation. Top candidates will also have experience with cardiology. 

Statistical Geneticist

Senior Data & Machine Learning Scientist

Principal Bioinformatics Scientist

Principal Data Scientist

Strong Analytics
Chicago
3 weeks ago
Strong Analytics is seeking a Principal Data Scientist to join our growing team! Principal Data Scientists take responsibility for designing, executing, and delivering successful projects for our clients. They play a "player coach" role. In this role, you will: Work with clients to scope project requirements and architecture Orchestrating the project team (data scientists, engineers, and dev-ops) to successfully build out the project roadmap

Database Developer

Kalderos
Chicago
5 months ago
+24
Build and maintain relational database artifacts and analytic tools that are high-performance, maintainable and flexible. Assist the Database Developer in planning for and acting on requirements for constant growth and expansion of Kalderos’ database environment. Quickly gain expertise with Kalderos’ existing database and business models and provide input on how to continually enhance both. Be the first line of support for questions about data quality and status.  Adhere to and promote software development standards and align them with changing technologies. Continually research and analyze new database frameworks and tools that Kalderos can bring to bear in its continuous improvement journey.

Data Scientist

Strong Analytics
Chicago
5 months ago
Statistical modeling and hypothesis testing. Designing, training, and validating results from a breadth of machine learning algorithms. Writing clean, efficient SQL. Integrating with various RDBMS (e.g., Postgres, MySQL) and distributed data stores (e.g., Hadoop). Building Python applications. Deploying applications into cloud-based infrastructures (e.g., AWS). Building deep neural networks with modern tools, such as PyTorch or Tensorflow. Creating and interacting with RESTful APIs. Managing *nix servers. Writing unit tests. Collaborating via Git.

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